As standalone paper drawings of system diagrams and flowcharts become obsolete, Models Based Systems Engineering (MBSE) is being implemented in more and more organizations as cyber-physical systems become more complex. It’s an innovation on a decades-old process. While traditional Systems Engineering is a collection of processes and analytic thinking to enable the management of complex systems, MBSE aims to virtualize these Systems Engineering best practices through modeling. The data integration and simulation of a system’s connected sub-systems and sub-components allows companies to use predictive analytics and methods to address risk and change impacts to the greater system – ultimately, mitigating the probability of “surprises” later on in production when it’s too late.

In their June 12th article, “Boeing To Unveil First New Midsize Airplane Details In Paris,” Aviation Week touched on how Models Based Systems Engineering is disrupting the development process of this new airplane. This new airplane is intended to close the gap in the market left by the end of 757 production in the early 2000s, providing service for roughly 250 passengers and ranges exceeding 5000 nautical miles. Boeing is counting on MBSE to allow it to accelerate development and speed their new product into service. Knowing first-hand the pitfalls of rushing a development program, what is it about MBSE that makes Boeing leaders confident that this program can defy the odds and meet such an aggressive schedule?

Traditional systems engineering is usually understood at a high-level – defining requirements while following a disciplined process of decomposition throughout the system and its subcomponents, consequently, then aggregating back through a series of testing and validation until the full system is vetted against the original fundamental requirements.

As a recent graduate from Boeing, NASA, and MIT’s Systems Engineering Professional Certificate program, I have a newfound insight into the extreme challenge this represents. To build any successful cyber-physical system (like an airplane), it takes a tremendous amount of subject-matter expert knowledge – the problem is, it’s hard to manage all that knowledge throughout the development process. As systems become smarter, more connected, more advanced and complex, we can’t continue to use traditional ways of evaluating and analyzing the system and still build it successfully.

Models Based Systems Engineering takes all of this detail knowledge and simulates how it affects all aspects of a design. By taking what was previously modeled on paper, combined with computing and analytics, MBSE digitizes the best practices of the industry. These overall system computative modeling techniques allow designers/developers to trace change propagation throughout the entire system, assessing and testing the risks and impacts against the rest of the design. This in turn, arms managers with more-informed and data driven decision making – crucial for a program as complex as brand new airplane.

In traditional project management these data driven decision making abilities aren’t possible until the majority of lifecycle costs have been locked in and changes become very expensive. Whereas a program implementing MBSE gains more system knowledge in the early phases of development before a majority of program costs are committed, giving leaders the facts they need to make program decisions before entering production. The ROI with MBSE allows for better decision making upfront, minimizing expensive program-delaying design changes later-on in production – surprises are less likely to “come out of nowhere” with MBSE.

For their newest airplane, Boeing has chosen MBSE as the foundation to defy these odds and deliver.  According to Aviation Week, Boeing is counting on MBSE to “significantly change the nonrecurring formula in terms of time, engineering error, disruption in the factory, quality, flow and the things you have to work to change an airplane.” No matter what the industry, integrating MBSE strategies into your organization allows for extensive iteration and experimentation, without costly early-run manufacturing and time-consuming physical testing.

Jordan Schiemer is a Systems engineer at Base2 Solutions. To learn more about implementing these best practices into your organization and other big ideas, visit our website

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